Chinese Journal of
Natural Chinese Journal of Natural Medicines 2017, 15(5): 03630374 Medicines
doi: 10.3724/SP.J.1009.2017.00363
Nuclear magnetic resonance based metabolomic differentiation of different Astragali Radix
LI Ai-Ping1, 2Δ, LI Zhen-Yu1Δ*, QU Ting-Li1, 3, QIN Xue-Mei1*, DU Guan-Hua1, 4
1 Modern Research Center for Traditional Chinese Medicine, Shanxi University, Shanxi 030006, China; 2 College of Chemistry and Chemical Engineering, Shanxi University, Shanxi 030006, China; 3 School of Pharmaceutical Science, Shanxi Medical University, Shanxi 030006, China; 4 Institute of Materia Medica, Chinese Academy of Medical Sciences, Beijing 100050, China
Available online 20 May, 2017
[ABSTRACT] Astragali Radix (AR) is one of the most popular herbal medicines in traditional Chinese medicine (TCM). Wild AR is believed to be of high quality, and substitution with cultivated AR is frequently encountered in the market. In the present study, two types of ARs (wild and cultivated) from Astragalus membranaceus (Fisch.) Bge. and A. membranaceus var. mongholicus (Bge.) Hsiao, growing in different regions of China, were analyzed by NMR profiling coupled with multivariate analysis. Results showed that both could be differentiated successfully and cultivation patterns or growing years might have greater impact on the metabolite compositions than the variety; the metabolites responsible for the separation were identified. In addition, three extraction methods were compared and the method (M1) was used for further analysis. In M1, the extraction solvent composed of water, methanol, and chloroform in the ratio of 1 : 1 : 2 was used to obtain the aqueous methanol (upper layer) and chloroform (lower layer) fractions, respectively, showing the best separation. The differential metabolites among different methods were also revealed. Moreover, the sucrose/glucose ratio could be used as a simple index to differentiate wild and cultivated AR. Meanwhile, the changes of correlation pattern among the differential metabolites of the two varieties were found. The work demonstrated that NMR-based non-targeted profiling approach, combined with multivariate statistical analysis, can be used as a powerful tool for differentiating AR of different cultivation types or growing years.
[KEY WORDS] Nuclear Magnetic Resonance; Chemical profiling; Astragali Radix; Cultivation patterns; Variety; Regions [CLC Number] R917 [Document code] A [Article ID] 2095-6975(2017)05-0363-12
Leguminosae family [1]. It has been shown to have immu- Introduction nostimulant, hepatoprotective, tonic, diuretic, antidiabetic ex- [2-4] Astragali Radix (AR), also known as Huangqi in China, pectorant, analgesic, and sedative properties . As a tradi- is dried root of Astragalus membranaceus (Fisch.) Bge. or A. tional folk medicine, it has been used for many therapeutic membranaceus var. Mongholicus (Bge.), belonging to the purposes in Asia, including in China, Korea, Japan, Mongolia and Siberia [5]. In addition to its medicinal use, it is also used in nutraceutical products, including herbal teas, soft drinks,
soups, and trail mixes [6-8]. Extensive chemial studies in recent [Received on] 27-Oct.-2016 [Research funding] This work was supported by the Ministry of years have revealed that the AR possesses various compounds, Agriculture for providing New Application for Herbal Research Grant including flavonoids, saponins, polysaccharides, and amino [9-12] Scheme (NRGS) (No. NH1014D040), the National 12th 5-Year Sci- acids . ence and Technology Support Program (No. 2011BA107B01), the A. membranaceus (Mojia in Chinese) is mainly distributed Science and Technology Innovation Team of Shanxi Province (No. in Heilongjiang (HLJ), Shandong (SD) and Sichuan (SC) of 2013131015), and the National Natural Science Foundation of China China, while A. membranaceus var. Mongholicus (Bge.) (No. 31570346). (Menggu in Chinese) is distributed mainly in the northern part [*Corresponding authors] Tel (Fax): 86-351-7011501, E-mail: qinxm@ of China, such as Shanxi (SX), Neimenggu (NM), Gansu sxu.eud.cn (QIN Xue-Mei); Tel (Fax): 86-351-7018379, E-mail: [email protected] (LI Zhen-Yu). (GS), and Shanxxi (SSX). Today, there are two types of ∆ These authors contributed to this work equally. growth pattern (cultivated and wild or semi-wild) for both These authors have no conflict of interest to declare. Menggu and Mojia AR. The wild or semi-wild AR is often
– 363 – LI Ai-Ping, et al. / Chin J Nat Med, 2017, 15(5): 363374 distributed in droughty mountainous areas, and grows more regions of China were collected and analyzed using 1H NMR- than 5 years before harvest, while the cultivated AR is often based metabolic profiling approach with various solvents to cultivated in wet and flat soil, and the growing years are only develop a differentiation method for different ARs. one year for Mojia AR and two years for Menggu AR. Materials and Methods Usually, the wild or semi-wild AR has longer and thicker roots, because of the longer growing years. In the market, AR Plant materials is graded by the root length, diameter, and physical 58 AR samples with two varieties of Astragalus (A. appearance: the longer and thicker the roots, the higher the membranaceus and A. membranaceus var. mongholicus, [13] quality . Due to the increasing demands of wild AR, the Mojia Huangqi and Menggu Huangqi in Chinese, respectively) substitution with cultivated AR is frequently encountered in were collected from different locations as shown on the map the drug market. According to the Chinese Pharmacopeia, (Fig. 1) and detailed information are included Table 1. All the astragaloside IV and calycosin-7-O-β-D-glucoside are used as chemical marker for quality control of AR. However, it is difficult to differentiate cultivated and wild AR by content determination of these marker compounds. Nowadays, metabolic fingerprinting has been widely used as a state-of-the-art technique in medicinal plant re- search [14]. Some recent studies have shown that the age of ginseng can be successfully discriminated by the metabolic fingerprinting coupled with the multivariate analysis. NMR has some unique advantages in metabolic fingerprinting stud- ies, such as rapidity, non-selectiveness, reproducibility, and stability [15-16]. In addition, detailed structural information of metabolites, including chemical shifts and coupling constants, can be directly obtained. This makes NMR an ideal choice for the profiling of the medicinal plants, such as ginseng [17-18], Tussilago farfara [19], Angelica acutiloba [20], and Artemisia afra [21]. In the present study, 58 AR samples from different cultivation Fig. 1 Geographical growing areas of Astragali Radix Table 1 List of Astragali Radix plant materials No. Youer no. Astragalus spp. Growing years Growing locations Cultivation patterns Source 1 HQ-HLJ-1 A membranaceus (Fisch.) Bge. over 5 Heilongjiang wild Field 2 HQ-HLJ-2 Mojia over 5 Heilongjiang wild - 3 HQ-HLJ-3 1 Heilongjiang, Hulan County cultivated - 4 HQ-HLJ-4 over 5 Heilongjiang wild Commercial 5 HQ-HLJ-5 over 5 Jiagedaqi wild Field 6 HQ-HLJ-5 over 5 Jiagedaqi wild Field 7 HQ-SD-1 1 Shandong, Wendeng cultivated Commercial 8 HQ-SD-1 1 Shandong, Wendeng cultivated Commercial 9 HQ-SD-1 1 Shandong, Wendeng cultivated Commercial 10 HQ-SD-1 1 Shandong, Wendeng cultivated Commercial 11 HQ-SD-1 1 Shandong, Wendeng cultivated Commercial 12 HQ-SD-1 1 Shandong, Wendeng cultivated Commercial 13 HQ-SX-22 A.membranaceus var. Mongholicus (Bge.) over 5 Shanxi, Hunyuan county wild Field 14 HQ-SX-23 Menggu over 5 Shanxi, Hunyuan county wild Field 15 HQ-SX-24 over 5 Shanxi, Daixian county wild Field 16 HQ-SX-25 over 5 Shanxi, Daixian county wild Field 17 HQ-SX-26 over 5 Shanxi, Yingxian county wild Field 18 HQ-SX-27 over 5 Shanxi, Yingxian county wild Field 19 HQ-SX-28 over 5 Shanxi, Yingxian county wild Field 20 HQ-SX-29 over 5 Shanxi, Yingxian county wild Field 21 HQ-SX-30 over 5 Shanxi, Hunyuan county wild Field 22 HQ-SX-31 over 5 Shanxi, Hunyuan county wild Field
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Table 1, Continued No. Youer no. Astragalus spp. Growing years Growing locations Cultivation patterns Source 23 HQ-SX-32 over 5 Shanxi, Tianzhen county wild Field 24 HQ-SX-33 over 5 Shanxi, Hunyuan county wild Field, 25 HQ-SX-34 over 5 Shanxi, Hunyuan county wild Field, 26 HQ-SX-35 over 5 Shanxi, Hunyuan county wild Field, 27 HQ-SX-37 over 5 Shanxi, Hunyuan county wild Field, 28 HQ-SX-36 over 5 Shanxi, Yanggao county wild Field, 29 HQ-SX-39 over 5 Shanxi wild Field 30 HQ-SSX-1 over 5 Shanxi, Zizhou county wild Locally bought 31 HQ-SSX-2 over 5 Shanxi, Yulin wild Locally bought 32 HQ-SSX-4 over 5 Shanxi, Yulin wild Locally bought 33 HQ-SSX-5 over 5 Shanxi, Yulin wild Locally bought 34 HQ-SSX-6 over 5 Shanxi, Yulin wild Locally bought 35 HQ-SSX-7 over 5 Shanxi, Yulin wild Locally bought 36 HQ-SSX-8 over 5 Shanxi, Yulin wild Locally bought 37 HQ-NM-7 2 Neimeng, Chifeng cultivated Commercial, 38 HQ-NM-3 2 Neimeng, Guyang county cultivated Commercial 39 HQ-NM-8 2 Neimeng cultivated Commercial 40 HQ-NM-9 2 Neimeng, Chifeng cultivated Commercial 41 HQ-NM-2 2 Neimeng, Shangdu county cultivated Commercial 42 HQ-NM-6 2 Neimeng, Guyang county cultivated Commercial 43 HQ-NM-10 2 Neimeng, Guyang county cultivated Commercial 44 HQ-NM-11 2 Neimeng, Xinghe county cultivated Commercial 45 HQ-GS-1 2 Gansu cultivated Locally bought 46 HQ-GS-2 2 Gansu, Longxi county cultivated Locally bought 47 HQ-GS-3 2 Gansu, Dangchang county cultivated Locally bought 48 HQ-GS-4 2 Gansu, Dangchang county cultivated Locally bought 49 HQ-GS-8 2 Gansu, Weiyuan county cultivated Field 50 HQ-GS-9 2 Gansu, Longxi county cultivated Field 51 HQ-GS-10 2 Gansu, Minxian county cultivated Field 52 HQ-GS-11 2 Gansu cultivated Locally bought 53 HQ-GS-16 2 Gansu, Minxian county cultivated Locally bought 54 HQ-GS-16 2 Gansu, Minxian county cultivated Locally bought 55 HQ-GS-19 2 Gansu, Longxi county cultivated Locally bought 56 HQ-GS-19 2 Gansu, Minxian county cultivated Locally bought 57 HQ-GS-22 2 Gansu, Weiyuan county cultivated Locally bought 58 HQ-GS-22 2 Gansu, Weiyuan county cultivated Locally bought
plant materials were authenticated by Prof. QIN Xue-Mei, China). Deuterated chloroform (CDCl3, 99.8% D) containing and the voucher specomens were deposited in the herbarium tetramethylsilane (TMS, 0.03%, m/V), methnol-d4 (99.8% D) of Modern Research Center for Traditional Chinese Medicine and D2O were obtained from Merck (Darmstadt, Germany). of Shanxi University. These samples were freeze-dried and Sodium 3-trimethlysilyl [2, 2, 3, 3-d4] propionate (TSP) was grinded to fine powders with a pestle and mortar and then from Cambridge Isotope Laboratories Inc. (Andover, MA, stored at −80 °C until analysis. USA), and NaOD was purchased from Armar (Dottingen, Solvents and chemicals Switzerland). Analytical grade chloroform, methanol, and acetone were Sample preparation purchased from Fengchuan Chemical Co. Ltd. (Tianjin, Three different extraction procedures were used in the
– 365 – LI Ai-Ping, et al. / Chin J Nat Med, 2017, 15(5): 363374 present study. In the first procedure (M1), a sample of 200 mg The regions of 4.70–5.02 and 3.281–3.360 were excluded of lyophilized powder were transferred into 10-mL glass from the analysis due to the presence of the signal from re- centrifuge tube and mixed with 6 mL of extraction solvent sidual signal of H2O and CD3OD, respectively. For spectral composed of water, methanol, and chloroform in the ratio of intensities from M2 and M3, they were reduced to integrated 1 : 1 : 2. At room temperature, the contents of the tube were regions of equal width (0.04) corresponding to the region of mixed thoroughly, sonicated for 25 min, and centrifuged 0.2–9.32 by MestReNova. The regions of 4.70–5.06 and –1 at 3 500 rmin for 25 min. The chloroform (lower layer) and 3.280–3.348 were excluded from the analysis because of the aqueous methanol (upper layer) fractions were transferred residual signal of H2O and CD3OD, respectively. For the separately into a 25-mL round-bottomed flask and dried with chloroform fraction, spectral intensities were scaled to TMS a rotary vacuum evaporator. Chloroform fractions were and reduced to integrated regions 0.04 ppm corresponding to dissolved in 800 μL of CDCl3, and aqueous methanol the region of 0.50–10.02. The region between 7.22 and fractions were dissolved in 800 μL of mixture (1 : 1) of 7.30 was removed from the analysis because of the residual CD OD and KH PO buffer in D O (adjusted to pH 6.0 by 3 2 4 2 signal of CHCl3. –1 1 molL NaOD) containing 0.05% TSP. The supernatants The remaining regions were normalized to the whole (600 μL) of all the samples were transferred into 5-mm NMR spectrum for principal component analysis (PCA), partial tube for NMR analysis after centrifugation for at 13 000 rmin–1 least squares discriminant analysis (PLS-DA), and orthogonal for or 15 min. projections to latent structures discriminant analysis In the second procedure (M2), 200 mg of lyophilized (OPLS-DA), which were performed in SIMCA-P software powder were transferred into 10-mL glass centrifuge tube and (version 13.0, Umetrics, Umeå, Sweden). All imported data mixed with 6 mL of extraction solvent composed of acetone were pareto-scaled for the multivariate analysis. Pareto and water in the ratio of 3 : 1. At room temperature, the scaling, in which each variable is divided by the square root contents of the tube were mixed thoroughly, sonicated for of the standard deviation, gives greater weight to the variables 25 min, and centrifuged at 3 500 rmin–1 for 25 min. The with larger intensity but is not as extreme as the use of supernatant was transferred into a 25-mL round-bottomed unscaled data. Pareto scaling is typically used when a very flask and dried with a rotary vacuum evaporator. The large dynamic range exists in the dataset [22-23]. Principal fractions were dissolved in 800 μL of CD3OD. The components analysis (PCA), which is an unsupervised supernatants (600 μL) of all the samples were transferred into clustering method requiring no prior knowledge of the data 5-mm NMR tube for NMR analysis after centrifugation at set that condenses the multivariate data into a reduced number 13 000 rmin–1 for 15 min. of variables called principal components, was initially The third method (M3) was similar to M2, except that the performed to examine the intrinsic variation in the dataset and sample was extracted with 6 mL of extraction solvent to obtain an overview of variation among the groups [24]. composed of chloroform and methanol in the ratio of 1 : 2. Orthogonal projections to latent structures discriminant NMR measurements analysis (OPLS-DA) were employed to maximize the 1H NMR was recorded at 25 °C on a Bruker 600 MHz separation between the groups and limit the impact of NMR AVANCE Ш NMR spectrometer (600.13 MHz proton data variation that is unrelated to sample class [25-26]. The 2 2 2 frequency). CD3OD and CDCl3 were used for internal lock quality of the models was described by R and Q values. R is purposes. Each 1H NMR spectrum was consisted of 64 scans defined as the proportion of variance in the data explained by requiring 5-min acquisition time with the following parameters: the models and indicates the goodness of fit. Q2 is defined as 0.18 Hz/point, pulse width (PW) = 30◦ (12.7 μs), and the proportion of variance in the data predictable by the [26] relaxation delay (RD) = 5.0 s. A presaturation sequence was model and indicates predictability . used to suppress the residual H2O signal with low power Relative amount of metabolites was evaluated based on selective irradiation at the H2O frequency during the recycle the integrated regions (buckets) of the NMR spectra and delay. FIDs were Fourier transformed with LB = 0.3 Hz. The ANOVA was performed in Excel to test the significance of resulting spectra were manually phased and baseline-corrected, differences in the metabolite levels among the samples of different and calibrated to TSP at 0.00 for water fractions and TMS at regions. The differences were tested on a 95% probability 0.00 for organic fractions and spectra were referenced to the level (P < 0.05). Hierarchical cluster analysis (HCA) and Pearson’s correlation (to test pairwise linear correlations for residual signal of CD3OD at 3.31 for only CD3OD redissolved fraction. the identified metabolites in Radix Astragali obtained from Data analysis different regions) analysis were performed by MetaboAnalyst The 1H NMR spectra were processed using MestReNova 2.0 (http://www.metaboanalyst.ca/, free of charge), a (version 8.0.1, Mestrelab Research, Santiago de Compostella, comprehensive tool suit for metabolomic data analysis. Spain). For aqueous methanol fraction of M1, spectral inten- Results and Discussion sities were scaled to TSP and reduced to integrated regions of equal width (0.04) corresponding to the region of 0.20– 9.20. As shown in Fig. 2, the AR crude drug from six different
– 366 – LI Ai-Ping, et al. / Chin J Nat Med, 2017, 15(5): 363374 regions of China showed different appearances in root length, 5.28, 5.4, 4.59, and 5.19 were assigned as anomeric protons diameter, and color. However, aqueous methanol fraction of of fructose, maltose, sucrose, α-glucose, and β-glucose in the these AR yielded similar 1H NMR spectra except SD samples. carbohydrate region, and fumaric acid ( 6.56, s), formic acid In addition, all the spectra were represented by high ( 8.47, s) were identified in the phenolic region. For the concentrations of primary metabolites such as amino acids, chloroform fractions, the dominant signals were from fatty sugars, and organic acids, but low in phenolic compounds acids or their esters, as revealed by the termial methyl ( (partly amplificated). 0.98), α-CH2 ( 2.3), β-CH2 ( 1.6), allylic CH2 ( 2.05),
and bis-allylic CH2 ( 2.77), all the other protons of hydrocarbon chain ( 1.2–1.3), and olefinic protons ( 5.35). The chemical shifts and coupling constants of all the identfied metabolites are sumarized in Table 2.
Fig. 2 Astragali Radix roots from 6 different regions
Metabolite identification The signals were assigned based on comparisons with the chemical shift of standard compounds using the chenomx NMR suite software, Human Metabolomics Database, as well as the reported literature data [27-28]. The signal overlap was partly resovled by the use of J-resolved spectra. The 1H NMR spectrum of AR can be divided into three distinct regions (Fig. 3). Organic acids, such as GABA, succinic acid, acetic acid, and citric acid, and several amino acids, such as valine ( 1.01, 1.06), alanine ( 1.48), N-acetyl-aspartate ( 2.83, 2.95), threonine ( 3.97), glutamine/glutamate ( 2.15, 2.49), and taurine Fig. 3 Representative 1H NMR spectrum of AR, the spectrum ( 3.51), were identifed in the corresponding organic acids was subdivided into three spectral regions (A, 0.3–2.9; B, and amino acids regions, respectively. The signals at 4.17, 3.0–4.6; and C, 5.0–9.2)
Table 2 Chemical shift assignments in aqueous extracts of Astragali Radix No. Metabolite Selected characteristic signals in NMR Assignment 1 Saponins 0.34 (s), 0.55 (s)
2 Valine 1.01 (d, 7), 1.06 (d, 7) γ, γ′-CH3, β-C
3 Threonine 1.34 (d, 6.6) γ-CH3
4 Lysine 1.47 (m), 1.73 (m), 1.89 (m) -CH2
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Table 2, Continued No. Metabolite Selected characteristic signals in NMR Assignment
5 Alanine 1.48 (d, 7.2) β-CH3 6 Arginine 1.6, 1.7, 1.9 (m), 3.24 (t, 7.0), 3.76 (t)
7 Acetic acid 1.94 (s) CH3
8 Proline 2.00 (m), 2.02–2.33 (m), 3.35 (t), 4.12 (m) α-CH, β-CH2, γ-CH2, δ-CH, δ′-CH
9 Glutamine/Glutamate 2.15 (m), 2.49 (m), 4.9 (s) β-CH2, γ-CH2, α-CH, COOH
10 GABA 2.3 (t, 7.2), 3 (t), α-CH, γ-CH2 2.42 (dd, 15.64, 9.33), 2.70 (dd, 15.53, 3.46), 11 Malate β'-CH, β-CH, α-CH, COOH 4.28 (dd, 9.15, 3.45), 2.43 (dd, 15.31)
12 Succinic acid 2.45 (s) CH2
13 α-ketoglutarate 2.45 (t, 6.9 Hz), 3.01 (t, 6.9 Hz) β-CH2, γ-CH2
14 Citrate 2.54(d, 16.56), 2.71(d, 16.41) α, α′-CH2, γ, γ′-CH2 15 N-Acetyl-Aspartate 2.83 (dd, 8.16, 16.94), 2.95 (dd, 3.97, 16.94)
16 malonate 3.13 (s) CH2
17 Choline 3.22 (s) N-CH3
18 Taurine 3.24 (t), 3.44 (t) CH2-N + 19 Betaine 3.27 (s), 3.9 (s) N(CH3) , CH2 20 Xylose 3.38 (t, 9.4), 4.54 (d, 6.7), 5.17 (d, 4.0) 21 Phenylalanine 3.44 (t, 9.5), 7.33(m) Ar-CH, Ar-CH 22 Glycine 3.68 (s) CH 23 β-Glucose 4.59 (d, 7.9) 1CH 24 α-Glucose 5.19 (d, 3.73) 1CH 25 Maltose 5.33 (d, 3.85) β'-CH, β-CH, α-CH, COOH 5.4 (3.83), 3.44 (dd, 9.5, 9.5), 3.75 (dd, 9.7, 9.5), 26 Sucrose 1CH, 2CH, 3CH, 4CH, 5CH, 6CH, 4.04 (dd, 10.2, 10.3), 4.17 (d, 8.64), 3.66 (s) 27 Raffinose 4.97 (d, 3.72), 5.45 (d, 3.6) 1CH, 2CH, 3CH, 5CH 28 Fumaric acid 6.53 (s) CH=CH 29 Adenine 8.21 (s), 8.26 (s) 1=CH, 4=CH 30 Formic acid 8.47 (s) CH 31 Ononin-7-glucoside 7.27 (d, 2.4), 7.19 (dd, 2.4, 9), 8.14 (d, 9), 7.0 (d, 9), 7.49 (d, 9) 6Ar-H, 7Ar-H, 8Ar-H, 2, 6-Ar-H, 3, 5-Ar-H 32 Calycosin-7-glucoside 7.27 (d, 2.4), 7.19 (dd, 2.4, 9), 8.14 (d, 9), 7.07(s) 6Ar-H, 7Ar-H, 8Ar-H, 2Ar-H
Multivariate data analysis (MvDA) (5 in wild, and 3 in cultivated), due to the fact that both wild To get a preliminary overview of the general similarities and cultivated AR are grown in NM. In addition, two GS and differences among the 58 collections, both aqueous samples from Dangchang County were located in the wild methanol and chloroform fractions of AR samples were first group. Most of the AR drugs from GS are cultivated in the analyzed by PCA, and a separation can be seen between the central Gansu province of Longxi, Weiyuan County, and there wild (including semi-wild) and cultivated samples in the score were also some wild ARs distributed in southern part of GS, plot of first three PCs (PC1: 25.9%; PC2: 15.7%; PC3: 11%) such as Dangchang and Minxian. for the aqueous methanol fracitons. However, for the For the Mojia AR from HLJ and SD, the PCA analysis of chloroform fractions, no obvious separation was observed the aqueous methanol fractions (Fig. 5A) revealed that the (Figs. 4A and 4B). This findings were futher confirmed by first two components accounted for 62% of the total variance HCA, antother unsupervized clustering method, and the and the HLJ samples can be separated from the SD samples results (Figs. 4C and 4D) were consistent with the PCA, by PC1. Furthermore, permutation tests were also performed 2 2 which also grouped the 58 AR samples into two clusters for to validate the PLS-DA model. All Q max and R values were aqueous methanol fractions. Both the PCA and HCA results higher in the permutation test than in the real model, reveal- suggested that cultivation patterns or growth years may have ing great predictability and goodness of fit (Fig. 5B). greater impact on the metabolite composition than the variety. OPLS-DA was used to reveal the differential metabolites The 8 NM samples are scattered in wild and cultivated groups between the Mojia AR from the two locations, and the cross
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Fig. 4 Three-dimensional score plots of a PCA performed to discriminate Astragali Radix from different regions. (A) aqueous methanol (upper) fractions; (B) the chloroform (lower) fractions; Dendogram of HCA using Ward's minimum variance method of 58 samples of the two varieties of Astragali Radix. (C) aqueous methanol (upper) fractions; (D) the chloroform (lower) fractions validated score plot and corresponding loading plots are observed between SX and SSX groups, other two extraction displayed in Fig. 5D, which showed that metabolites methods (M2 and M3) were applied to see whether better such as betaine, α-glucose, arginine, and citrate were separations could be achieved. The three methods were com- higher in HLJ samples, and the levels of raffinose, pared by PCA using 6 individual samples. In M1 procedure, aspartate, succinate and glutamine/glutamate were higher 18 AR samples could be divided into three groups: SX group , in SD samples. GS group and SSX group. In M2, the SX and SSX groups For the Menggu AR, as both wild and cultivated ARs were merged into one group. In M3, three groups could be were distributed in NM, the NM samples were excluded in oberved, but partial overlap was shown. Thus, the M1 was further analysis. The PCA analysis of the AR samples from better than the other two methods and used to find the SX, SSX, and GS (PC1: 27.5%; PC2: 13.1%) showed that GS differential metabolites of Menggu AR from different samples (cultivated, 2 years) were on the negative side of PC2, locations. and the SX samples and SSX samples (wild, over 5 years) For the SX and GS samples, the OPLS-DA results were located in the positive side of PC2, which could be fur- showed that SX samples contained more betaine, ther separated by PC1. The separation between the GS sam- N-Acetyl-aspartate, malate, succinic acid, taurine, β-glucose, ples and SX/SSX samples was more remarkable than those and citrate, while the GS samples contained more GABA, between the SX and SSX samples. As partial overlap was glycine, sucrose, arginine, and phenylalanine. For the
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Fig. 5 (A) PCA score plot showing the grouping of Mojia AR according to cultivated regions (HLJ and SD) for aqueous methanol (upper) fractions, (B) permutation test for 200 times, (C) OPLS-DA score plot, (D) S-plot
SSX and GS samples, the SSX samples contained more and some unidentified metabolites (3.64, 3.52, 4.08). In citrate, taurine, betaine, malate, α-glucose and β-glucose, but addition, M2 was characterized by higher levels of choline, less phenylalanine, proline, GABA, lysine, N-Acetyl-aspartate, xylose and some amino acids such as valine, proline and arginine, glycine, acetate, glutamine/glutamate and sucrose threonine and M3 was characterized by higher levels of ar- than the GS samples. The results suggested that betaine, ginine and taurine. N-Acetyl-aspartate, malate, succinic acid, taurine, β-glucose, Metabolite quantification α-glucose, and citrate were accumulated in wild AR, and The differential metabolites as revealed above were GABA, glycine, sucrose, lysine, arginine, phenylalanine, relatively quantified using bucket data of 1H NMR acetate, and glutamine/glutamate were accumulated in spectrometry. As shown in Fig. 6, the concentration of cultivated AR. metabolites in different AR samples varied greatly. Compared The differential metabolites between the two types of with cultivated AR, wild AR contained more betaine, xylose, wild samples were also determined. Compared with the SSX α-glucose, arginine, malate, N-Acetyl-Asparate, maltose, citrate, group, the SX group contained more betaine, N-Acetyl-aspartate, β-glucose, taurine, valine and calycosin, but less sucrose, proline, GABA, glutamine/glutamate, malate, glycine, lysine, phenylalanine, alanine, and adenine. In addition, the ratio of and less arginine, sucrose, citrate, and taurine. sucrose/glucose (both and form) were calclulated for PCA was further applied to reveal the difference of different AR samples. As shown in Fig. 7, the cultivated AR metabolic profiles between the different extraction samples from SD (Mojia AR) and GS (Menggu AR) exhibited methods. For the SX samples, the extracts were clustered extremly higher sucrose/glucose ratio than those of wild according to the type of solvents used in the extraction (including semi-wild) AR from HLJ (Mojia AR), SSX methods, and M1 could be separated from M2 and M3 by (Menggu AR) and SX (Menggu AR). Thus, the sucrose/ PC1, while M2 and M3 could be further separated by glucose ratio can be used as an simple method for differentiating PC2. For the SSX and GS samples, the extraction meth- ARs of different cultivation types or growing ages. ods resulted in the same clustering pattern as those of SX. Fig. 8 shows the correlation matrices [29] separately for 5 Interpretation of the corresponding loading plot of SX groups as built from the Pearson s correlations among the 16 samples revealed that extracts from M1 were dominated metabolites detected in the AR samples and facilitated by by higher amount of phenylalanine, glycine, betaine, sucrose, using scaled colors. The correlations among the metabolites
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Fig. 6 Quantification of identified metabolites selected by ANOVA (P < 0.05) in AR extracts analyzed by 1H NMR for 2 different cultivation patterns (wild vs cultivated) were generally positive for the Mojia AR, but many negative tyl-Aspartate, β-glucose and alanine were negative, while for correlations were observed for Menggu AR by visual analysis. cultivated AR, all these correlations were negative. It is not Changes in correlation pattern (from positive to negative or clear how or why the metabolites correlations changed in such vice versa) were observed for α-glucose (correlated to betaine, a way, due to cultivation pattern and genetic variations. phenylalanine and citrate), taurine (correlated to alanine and In the present study, AR from one species with two phenylalanine), citrate (correlated to α-glucose, sucrose and varieties of Astragalus (A. Membranaceus and A. membranaceus betaine) when comparing between the two varieties. In addi- var. mongolicus) grown in different regions of China, were tion, for the wild AR, the correlations of α-glucose/β-glucose and compared and characterized based on multivariate statistical alanine, sucrose/β-glucose were positive, and maltose/N- Ace- analysis of 1H NMR based metabolomic data. 29 Primary
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metabolites, including amino acids, organic acids, sugars, and 3 secondary metabolites were identified without chromatographic separation. Multivariate analysis showed that the different AR samples could be differentiated successfully, and the cultivation pattern or growth years might have greater impact on the metabo- lite composition than the variety. Compared with the cultivated AR, the wild AR accumulated more betaine, xylose, α-glucose, arginine, malate, N-Acetyl-Asparate, maltose, citrate, β-glucose, taurine, valine and calycosin, but less sucrose, phenylalanine, alanine and adenine. And the sucrose/glucose ratio can be used to differentiate the wild and cultivated AR. The results obtained in the present study demonstrated that NMR-based non-targeted profiling approach, combined with multivariate statistical analyses, can be Fig. 7 The sucrose/glucose ratios for AR of 5 different used as a powerful tool for differentiating the AR of different cul- regions based on their integrated area tivation type or growth years.
Fig. 8 Pearson s correlation matrices of 16 metabolites of AR from 5 different regions. The color scale is relative to the Pearson s correlation coefficients. 1. sucrose, 2. betaine, 3. phenylalanine, 4. xylose, 5. α-glucose, 6. arginine, 7. malate, 8. N-Acetyl-Aspartate, 9. maltose, 10. citrate, 11. β-glucose, 12. alanine, 13. taurine, 14. adenine, 15. valine, 16. calycosin
Plants often accumulate specific secondary metabolites in of the environmental factors and how much such chemical response to abiotic and biotic stresses [30-31]. The accumulation composition differences have an impact on the bioactivities of of different metabolites in the ARs of different regions may AR. reflect their adaptation to different environments. However, it is In the present study, most of the compounds identified not possible at present to ascertain the relative contributions were primary metabolites, and further studies should be
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Cite this article as: LI Ai-Ping, LI Zhen-Yu, QU Ting-Li, QIN Xue-Mei, DU Guan-Hua. Nuclear magnetic reso- nance based metabolomic differentiation of different Astragali Radix [J]. Chin J Nat Med, 2017, 15(5): 363-374
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